Elamo-Dravidian and the Koraga

Novel 4,400-year-old ancestral component in a tribe speaking a Dravidian language:

Research has shown that the present-day population on the Indian subcontinent derives its ancestry from at least three components identified with pre-Indo-Iranian agriculturalists once inhabiting the Iranian plateau, pastoralists originating from the Pontic-Caspian steppe and ancient hunter-gatherer related to the Andamanese Islanders. The present-day Indian gene pool represents a gradient of mixtures from these three sources. However, with more sequences of ancient and modern genomes and fine structure analyses, we can expect a more complex picture of ancestry to emerge. In this study, we focus on Dravidian linguistic groups to propose a fourth putative source which may have branched out from the basal Middle Eastern component that gave rise to the Iranian plateau farmer related ancestry. The Elamo-Dravidian theory and the linguistic phylogeny of the Dravidian family tree provide chronological fits for the genetic findings presented here. Our findings show a correlation between the linguistic and genetic lineages in language communities speaking Dravidian languages when they are modelled together. We suggest that this source, which we shall call ‘Proto-Dravidian’ ancestry, emerged around the dawn of the Indus Valley civilisation. This ancestry is distinct from all other sources described so far, and its plausible origin not later than 4,400 years ago on the region between the Iranian plateau and the Indus valley supports a Dravidian heartland before the arrival of Indo-European languages on the Indian subcontinent. Admixture analysis shows that this Proto-Dravidian ancestry is still carried by most modern inhabitants of the Indian subcontinent other than the tribal populations. This momentous finding underscores the importance of population-specific fine structure studies. We also recommend informed sampling strategies for biobanks and to avoid oversimplification of ancestral reconstruction. Achieving this requires interdisciplinary collaboration.

Not definitive, but I think this shows the value of greater sampling in Indian subcontinental populations.

Bengalis are all basically very similar (except for Brahmins)

The new paper, 50,000 years of Evolutionary History of India: Insights from ~2,700 Whole Genome Sequences, is very good. It also answers a question that comes up sometimes: how different are West Bengalis from Bangladeshis? We haven’t had a apples to apples comparison until this paper that’s easy to understand.

There are figures in the paper that make the overlap clearer. The main difference is more variance in the West Bengalis, and a greater East Asian shift among Bangladeshis. But the latter is clearly just geography; those whose ancestry is from the east of the Padma (like me) always have more East Asian ancestry than those from the west, while those in the north also seem to have more.

The variance in West Bengal is probably driven by caste. You can see Brahmins, and probably what are Bengali-speaking scheduled castes and tribes. In the Bangladesh Muslim population everyone eventually intermarried.

The Assamese are even more East Asian shifted than the Bangaldeshis. As I said in a previous post, these Indo-Aryan groups look like they mixed with a Khasi-like population at some point.

Finally, the West Bengal population had admixture from an East Asian group between 500 and 600 AD. This is the same date as for the Bangladeshis, meaning they are both the same population with the same origin. The major difference seems likely to be the proportion of East Asian ancestry and lack of caste structure within eastern Bengal.

Sri Lanka Genetics

Reconstructing the population history of Sinhalese, the major ethnic group in Śrī Laṅkā:

Interestingly, we found an unexpected excess of smaller chunks sharing between Marāṭhā and Sinhala (>16%) than the Marāṭhā and STU, thus supporting the linguistic hypothesis of Geiger, Turner and van Driem. To confirm the excess sharing, we looked for the population which was sharing maximum IBD with Sinhala and STU.

Looks like confirmation of Sinhala western Indian origins rather than eastern Indian origins.

Population structure in South Asian – Genomes Asian 1K paper

The full version of this paper is out, South Asian medical cohorts reveal strong founder effects and high rates of homozygosity. It’s not the best for understanding population structure because they focus on within South Asia variation, but it does seem to confirm that among Bengalis there is a cline from west to east, irrespective of religion (see the discussion where they note that Muslims in the west cluster with westerners). I found a PCA in the supplements where I added some explanatory notes. It’s really hard to parse their figures because they really didn’t care, and the Genomes Asia Consortium doesn’t release their data… (their browser sucks)

Perhaps the Indus Valley Civilization did descend from Zagrosian farmers?

On the limits of fitting complex models of population history to f-statistics:

These results show that at least with regard to the AG analysis, a key historical conclusion of the study (that the predominant genetic component in the Indus Periphery lineage diverged from the Iranian clade prior to the date of the Ganj Dareh Neolithic group at ca. 10 kya and thus prior to the arrival of West Asian crops and Anatolian genetics in Iran) depends on the parsimony assumption, but the
preference for three admixture events instead of four is hard to justify based on archaeological or other arguments.

Why did the Shinde et al. 2019 AG analysis find support for the IP Iranian-related lineage being the first to split, while our findGraphs analysis did not? The Shinde et al. 2019 study sought to carry out a systematic exploration of the AG space in the same spirit as findGraphs—one of only a few papers in the literature where there has been an attempt to do so—and thus this qualitative difference in findings is notable. We hypothesize that the inconsistency reflects the fact that the deeply-diverging WSHG-related ancestry (Narasimhan et al. 2019) present in the IP genetic grouping at a level of ca. 10% was not taken into account explicitly neither in the AG analysis nor in the admixture-corrected f4-symmetry tests also reported in Shinde et al. (2019).

Cousin marriage in Bangladesh


This piece arguing for the end to cousin marriage in the UK in The Times (driven by Pakistanis) took me to a paper in PLOS One, Genetic and reproductive consequences of consanguineous marriage in Bangladesh:

The mean prevalence of CM in our studied population was 6.64%. Gross fertility was higher among CM families, as compared to the non-CM families (p < 0.05). The rate of under-5 child (U5) mortality was significantly higher among CM families (16.6%) in comparison with the non-CM families (5.8%) (p < 0.01). We observed a persuasive rise of abortion/miscarriage and U5 mortality rates with the increasing level of inbreeding. The value of lethal equivalents per gamete found elevated for autosomal inheritances as compared to sex-linked inheritance. CM was associated with the incidence of several single-gene and multifactorial diseases, and congenital malformations, including bronchial asthma, hearing defect, heart diseases, sickle cell anemia (p < 0.05). The general attitude and perception toward CM were rather indifferent, and very few people were concerned about its genetic burden.

A rate around 5% is in line with my intuition and what I’ve seen elsewhere, though there is wide variance by locality. The best thing about the paper is the chart above, the offspring of first cousin marriage have mortality rates 3 times greater than non-cousin marriages. There are other numbers relating to disease, etc. The paper is good because it’s from a developing country without world-class healthcare (though no longer a total basketcase) so you can see disease risk plainly.

More generally in relation to “cousin marriage”

– I have seen “outbred” Pakistani genomes that look like the product of cousin marriage due to the practice’s frequently earlier on in the pedigree

– This is comparable to some Indian caste groups that practice exogamy (North Indian) on the jati level. The jati has been endogamous so long that everyone has become a second cousin…

Showing an early entry of steppe ancestry into India

Introduction

A common claim that can be increasingly found in the Indic internet is that the steppe ancestry found in modern day Indians with significant frequency entered India in the late Iron Age and/or the Early Historic Period. Dr Niraj Rai has implied as much in interviews, and Ashish has championed this theory, recently identifying a sample in Iron Age Turkmenistan as an example source of ancestry for modern day Indians.

I previously responded to these claims on Twitter and am here restating my arguments together with some additional analyses. To begin with, we must understand the geography of gene flow from the steppe, whether via migrations or via inter-marriages.

Geography of migrations

Map showing Gandhara and Swat, with archeological mountain sites in Swat valley North of Gandhara

Here are some maps of the northern end of the Indian subcontinent. Notably, the Hindu Kush mountains formed a barrier between Gandhara and the areas north of it – travel through this area in large numbers was quite difficult. Instead, travelers from the steppe would travel around the western tip of the Hindu Kush mountains, heading southeast from Balkh to Kabul/Begram through semi-mountainous lands, and from there heading east down the Kabul river valley into the Vale of Peshawar via the Khyber Pass, to the city of Pushkalavati at the Bala Hisar / Charsada sites. From there, they could head down the Indus Valley or more commonly further east to Taxila, before continuing on towards the Ganga Valley. An alternate route would travel around the semi-mountain regions of Afghanistan, heading south from Herat to Kandahar, and then southeast from there via the Bolan Pass into the middle of the Indus Valley (i.e. roughly the Punjab-Sindh border).

Either way, the Swat Valley in the mountains north of Gandhara was not a stopping point along the route into India. Furthermore, the Swat Valley was not directly part of the general Indian geographic sphere, which extended up to about Shahbazgarhi. In many ways Swat’s relationship to the Indus Valley was akin to Nepal’s relationship to the Ganga Valley – significant trade and cultural contact but also some degree of genetic differentiation.

As such, we would expect steppe ancestry to have entered in greater proportions into Indo-Gangetic Plains than in Swat – especially into Punjab. In fact, that’s exactly what we observe in modern populations. The highest steppe ancestry modern populations are Punjabi / Haryanvi Rors and Jats.

To ascertain the timing of steppe admixture, ideally we’d have ancient DNA samples from the relevant time periods in these regions to check directly for steppe admixture. However, due to a mixture of climate issues, underfunded archeology, and a culture of cremation, there is a total dearth of relevant ancient DNA samples. Instead, we must rely on what samples we’re able to find and utilize the DATES tool to estimate admixture times.

DATES Estimates

Interpreting / theory

Now, to interpret DATES results, we must keep in mind particularly with an incompletely admixed population such as India’s, that admixture times can be much later than migration times. When Indian-residing groups with elevated steppe ancestry interbreed with those with low steppe ancestry, their intermediate steppe ancestry offspring will show more recent admixture. This does not mean the steppe migration occurred at the time of admixture, but rather that admixture continued after migration occurred. As such, admixture times are lower bounds, not mean estimates, for the timing of migration. In the Indian context, we must look to older samples as well as groups with early caste endogamy to discern the true time of migration, without the confounding effects of later intermingling.

Additionally, when modeling with DATES, preference should be given to the model that provides the narrowest estimates. Per Chintalapati et. al., a model is considered to be valid if the Z-score is > 2, the normalized root mean square deviation is below 0.7, and estimated number of generations is below 200.

To model the sources of admixture in DATES, I’ve used Sintashta-Petrovka samples for the steppe source (both sets of Sintashta samples as well as the Petrovka sample available in the Reich database) against the AASI-proxy used by Narasimhan et. al. (STU.SG, ITU.SG, BIR.SG) plus Irula.DG and Pallan-like Roopkund outliers. Using the relatively pure Sintashta-Petrovka samples instead of Central_Steppe_MLBA particularly reduces the noisiness of DATES modeling in the single target sample modeled later here.

We can sanity check this model by testing admixture times for steppe-enriched Iron Age Swat samples and ensure the results are calibrated in line with the Narasimhan paper:

Graph showing DATES curve for SPGT

mean: 27.970 std error: 2.691 Z: 10.394
nrmsd: 0.055
Sample date estimate: 920 BCE
95% interval admixture estimate: 1853-1552 BCE

This yields a good fit that’s pretty much identical to the Narasimhan paper and indicates that steppe ancestry entered the Swat Valley in the first half of the 2nd millennium BCE.

In Roopkund

To find a bound on the timing of admixture in mainland India, we can examine one of the few sets of premodern DNA samples – namely, a collection of pilgrims  who had succumbed to hailstorms in the 8th-10th centuries CE in Roopkund Lake. The skeletons sequenced here had a variety of steppe ancestry and included several individuals with relatively high steppe ancestry who clustered with modern day Brahmin Tiwaris.

Graph showing DATES curve for Roopkund A

mean: 84.592 std error: 10.206 Z: 8.288
nrmsd: 0.100
Sample date estimate: 850 CE
95% interval admixture estimate: 2091-948 BCE

The fit is excellent and the results are highly statistically significant. We see clear evidence that the Roopkund samples obtained their steppe admixture in the 2nd millennium BCE and became relatively genetically isolated by the start of the 1st millennium BCE.

In Loebanr outlier

Now, we can look at one outlier Iron Age woman from the Swat culture who had particularly high steppe ancestry, and appeared to be an individual at the far end of the ANI cline. This woman proved to be a better proximal source of steppe ancestry for modeling modern day Indians than her Turkmenistan contemporary (another single sample that has been proposed as a source of late steppe ancestry). Where did this woman come from? Punjab would be a good bet. After all, her significant amount of AASI in combination with a relatively low Anatolian neolithic ancestry argues against a location in Central Asia. And modern day Punjabi / Haryana Jats and Rors are not far removed from her – e.g. I modeled a Haryanvi Ror sample as 16% Irula and 83% ancestry from a population akin to this woman. Therefore, it’s likely she was a migrant up from Gandhara or further south and can be used as a representative of higher caste Punjabis of her time.

Let’s look at the DATES modeling for this woman:

Graph showing DATES curve for Loebanr outlier

mean: 37.593 std error: 13.239 Z: 2.840
nrmsd: 0.191
Sample date estimate: 920 BCE
95% interval admixture estimate: 2714-1231 BCE

As is normal for a single sample, the data is somewhat noisy. Nevertheless, DATES is designed to be able to handle single target samples, and we have a good nrmsd score and a statistically significant result, albeit with a wide range. This would confirm that the woman came from a large population that had been well formed by the late 2nd millennium BCE. More crucially, the weighted covariance at large genetic distance is close to 0, indicating she was not for example a product of recent marriage between a high steppe migrant from Turkmenistan and a lower steppe inhabitant of Loebanr. However, let’s obtain a narrower estimate of admixture time.

IVC-related as source

To improve the fit, in light of the low AASI proportion in the Loebanr outlier, we can use IVC and similar individuals high in neolithic ancestry but lacking in steppe ancestry as the source. For this group, I’ve used the IVC periphery samples in the Reich dataset, along with Aligrama (Iron Age Swat samples without steppe ancestry), and SiS-BA-1 (non-Indus-periphery samples from the Helmand culture, which have India-related ancestry).

Once again, let’s check calibration against the results from the Narasimhan paper:

mean: 24.077 std error: 2.658 Z: 9.059
nrmsd: 0.101
Sample date estimate: 920 BCE
95% interval admixture estimate: 1743-1445 BCE

The nrmsd is somewhat worse but the results are essentially in line with the modeling using AASI-rich sources.

Now, let’s give it a go on the Loebanr outlier woman:

mean: 27.621 std error: 5.222 Z: 5.290
nrmsd: 0.356
Sample date estimate: 920 BCE
95% interval admixture estimate: 1986-1401 BCE

Due to noise, nrsmd worsened but is still well below 0.7. Notwithstanding this though, the shape of the curve fits like a glove and appears spot on with the average weighted covariance. And that good curve fit is reflected in the improved Z score and lower standard error. The result lets us conclude that the Loebanr outlier woman received her steppe ancestry admixture at roughly the same time as her Swat Valley contemporaries did.

Conclusion / Implications

To conclude, we’ve found evidence that high steppe ancestry may have reached the Ganga Valley by the end of the 2nd millennium BCE, and likely had reached  Gandhara / Punjab by the middle of the 2nd millennium BCE. Some of the steppe ancestry that entered Gandhara also traveled up into the Swat Valley in the same timeframe.

All of this evidence is consistent with steppe ancestry settling in the Punjab centuries prior to the composition of the Rigveda there, in conjunction with the observed spread of R1a-L657 in India which originated from the R1a-Z93 Y-haplogroup of the steppe. It’s also consistent with the beginning of formation of caste groups in the Kuru-Panchala Kingdoms around the time the varna system began to be implemented in the Iron Age Late Vedic Period.

We may also hypothesize that perhaps the people of the Swat Valley spoke old Burushaski. After all, the modern day Burusho people are located in the mountains further uphill from the Swat Valley, and genetically have some traits in common with the non-outlier samples of the Swat – viz. lower Sintashta ancestry and elevated IAMC (Aigyrzhal-like Inner Asian Mountain Corridor) ancestry. They have additional East Asian ancestry but this is consistent with a population that would have had trade links to the Tarim Basin, and the observed presence of Turkic and Tibetan loanwords in the Burusho language.

Note that while the evidence here indicates that there had already been substantial steppe admixture into India in the Bronze Age, it does not preclude additional later admixture of steppe ancestry in the Iron Age or Early Historic Period. Substantial admixture in this period is unlikely for a few reasons: lack of admixture from East Asian or Anatolian heavy groups (why would the groups resembling earlier steppe populations be the only ones to admix into India?), lack of migration of newer steppe-originated Y chromosome lineages, and the sheer size of the growing Indian population which would lessen the relative genetic contribution of migrants. But regardless though, the presence or absence of additional late steppe admixture does not have much of a bearing on the debate regarding the origins of the Indo-Aryan languages.

Brown Pundits